We completed genotyping subjects with both the Affymetrix 100,000 SNP chip and the 1 million SNP chip technologies. Phase 1 was designed to detect potential associations with young-onset type 2 diabetes, by genotyping 300 early-onset type 2 diabetes subjects (onset age<25 yrs) and 329 non-diabetic controls (age >45 yrs), and 271 additional subjects who were diabetic and non-diabetic siblings of the selected subjects. Associations with diabetes were calculated using both a case/control analysis (N= 629) and a within-family analysis (482 siblings from 169 sibships), and SNPs that had the strongest association for the combined associations were prioritized. Phase 2 of the GWA was designed to detect associations with pre-diabetic traits (% body fat, insulin action as measured by the hyperinsulinemic euglycemic clamp technique, and the acute insulin response to an intravenous bolus of glucose). Six hundred non-diabetic subjects who had been metabolically phenotyped for these predictors of diabetes were genotyped. Measures of BMI were available on all samples from Phase 1 and Phase 2. SNPs that provided the best associations for diabetes and/or a pre-diabetic trait (including BMI) were selected for additional genotyping in a population-based sample of 3501 full-heritage Pima Indians. This genotyping utilized a new high throughput technology (Bead Express). We recently completed genotyping the best signals from our genome-wide association analysis in a sample of 3501 full hertiage Pima Indians and are currently replicating the best SNPs from this sample in a second population-based sample of 3784 mixed heritage Native Americans. To date the strongest assocations in the combined samples for diabetes are with SNPs in DNER (P= 1 x 10-8) and with KCNQ1 (P= 5 x 10-9). The strongest associations in the combined samples for BMI are with SNPs in BRD2, HEATR5B, UBE2E, GSTA5, NOVA1, FTO, MAP2K3, and CYB5A (all P between 10-6 and 10-7). Studies are ongoing to identify the casual variant that underlies each of these associations. One of our strongest findings was identifying MAP2K3 as a new gene for obesity. This gene had not been reported as being among the top signals in published GWASs from other ethnic groups, however, we requested that the GIANT study of BMI in Caucasians look at specific SNPs in their GWAS data and several SNPs did have significant associations with BMI (P = 2 x 10-4). The effect of these variants was larger in American Indians as compared to Caucasians. Combining our American Indian data with the Caucasian data provided strong associations (P = 4 x 10-9). Functional studies on MAP2K3 showed that this gene has a role in adipogenesis, which is consistent with what is currently known about MAP signaling pathways. However, we also show that constitutive expression of MAP2K3 in the hypothalamus, a key tissue for modulating food intake, is associated with an up-regulation of genes involved in inflammation. This is an intriguing finding because several recent reports have proposed a causal role of hypothalamic inflammation in high fat induced obesity, as well as cytokines eliciting effects on feeding behavior.